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Social interactions and beyond
This document discusses social networks and interactions. It covers topics like the growth of social media usage, how networks can be represented and analyzed through graphs, common network analysis metrics like centrality and community detection, and tools for network analysis. Examples of networks that can be studied include social networks from social media platforms, collaboration networks, and information diffusion networks.
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Social interactions and beyond
- 1. Social Interactions ... …and beyond Francisco Restivo fjr@fe.up.pt slideshare.net/frestivo
- 2. Topics • The explosion ofsocial interactions • ICT and social interactions • Social Networks • Metrics • Data • Tools • Project ideas 2SSIIM, 2017/10/12
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- 7. Where are wenow? 7SSIIM, 2017/10/12
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- 9. SSIIM, 2017/10/12 9 Socialmedia usage 2016
- 10. How teens communicate SSIIM,2017/10/12 10
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- 12. Networks • Networks are everywhere • Social,biological, financial, etc • Complex networks • Communities reveal properties of networks • Contagion • Controversies • Society! 12SSIIM, 2017/10/12
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- 15. Social networks Social interactions • Like,comment, share, cite • e-Commerce • Payments • Digital marketing • Political marketing • etc 15SSIIM, 2017/10/12
- 16. Where are we? ● Complexnetworks ● Actors influencing and being influenced by other actors ● But humans are not software agents ● Difficult to establish consensus ● Intelligence highly needed ● Maybe biology could inspire us... SSIIM, 2017/10/12 16
- 17. SO?! SSIIM, 2017/10/12 17 • Let'shave a look at graphs and networks
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- 19. Euler 1707 -1783 SSIIM, 2017/10/12 19
- 20. Basics of graphsand networks • G = (V, E) • O(G) = |V| order • S(G) = |E| size • A adjacency matrix • Ki degree of vertex i • Directed/undirected SSIIM, 2017/10/12 20
- 21. Representation of networks • Matrixes,graphs, edge lists, etc A B C D E A 0 1 1 1 0 B 1 0 1 0 1 C 0 0 0 1 0 D 0 1 1 0 0 E 1 1 0 0 0 A B A C A D B A B C B E C D D B D C E A E B SSIIM, 2017/10/12 21
- 22. • Equivalence relations – Reflexive,symmetric, transitive – Equivalence classes • Order relations (partial, total or linear) – reflexive, anti-symmetrical, transitive – Hasse diagrams – x,y xRy yRx (total) SSIIM, 2017/10/12 22 a b x taller than y Be born in the same year Live in the same street Binary relations
- 23. SSIIM, 2017/10/12 23 Hassediagram m divides n
- 24. SSIIM, 2017/10/12 24 AOPO LIS MAD PAR LON BER A^2 OPO LIS MAD PAR LON BER A^3 OPO LIS MAD PAR LON BER OPO 0 1 1 1 0 0 OPO 3 0 1 1 2 2 OPO 2 7 6 6 1 1 LIS 1 0 0 0 1 1 LIS 0 3 2 2 0 0 LIS 7 0 2 2 5 5 MAD 1 0 0 1 0 1 MAD 1 2 3 1 1 0 MAD 6 2 2 5 3 5 PAR 1 0 1 0 1 0 PAR 1 2 1 3 0 1 PAR 6 2 5 2 5 3 LON 0 1 0 1 0 0 LON 2 0 1 0 2 1 LON 1 5 3 5 0 1 BER 0 1 1 0 0 0 BER 2 0 0 1 1 2 BER 1 5 5 3 1 0 opo lis mad par lon par parber Composition of relations
- 25. • Usually not transitive(a likes b and b likes c but ...) • “Equivalence” relations – No equivalence classes – But communities, clusters, etc • “Order” relations (partial, total) – No Hasse diagrams – Rankings, proeminence indexes, etc SSIIM, 2017/10/12 25 Real life relations
- 26. Global metrics • Number ofvertexes 5 • Number of edges 11 • Number of components 1 • Diameter 2 • Density 0.55 SSIIM, 2017/10/12 26
- 27. Centrality Measures • Degree centrality –Edges per node (the more, the more important the node) • Closeness centrality – How close the node is to every other node • Betweenness centrality – How many shortest paths go through the edge node • Bibliometric + Internet style (quality of edges) – PageRank, eigenvector 27SSIIM, 2017/10/12
- 28. Champions league Pagerank SSIIM,2017/10/12 28
- 29. Common Tasks • Measuring “importance” –Centrality, prestige, influence (incoming links) • Diffusion modeling – Epidemiological • Clustering – Blockmodeling, Girvan-Newman, Chinese whisper • Visualization/Privacy/etc. 29SSIIM, 2017/10/12
- 30. Community detection • Communities andclusters are different • Network data is related to graph properties • Real world data is big SSIIM, 2017/10/12 30
- 31. Modularity • Compares number ofedges with number of edges of a random network • Maximize Q is NP-hard SSIIM, 2017/10/12 31 jC,iCδ ij ijPijA m2 1 Q m2 jkik ijP
- 32. Dynamics • Networks have atemporal dimension • Interactions – follow, like, share, mention, retweet, hashtag, etc – occur in sequence • Network properties evolve in time SSIIM, 2017/10/12 32
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- 43. Digital Methods Initiative SSIIM,2017/10/12 43
- 44. Queen @ Spotify SSIIM,2017/10/12 44
- 45. Queen @ Spotifyin Gephi SSIIM, 2017/10/12 45
- 46. #barcelona @ Tumblrin Gephi SSIIM, 2017/10/12 46
- 47. Ideas? ● Find and useAPIs ● Crawl Instagram ● Hashtags co-occurrences (Twitter, Tumblr) ● Detect fake/abnormal behaviours ● Use your imagination! SSIIM, 2017/10/12 47
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